More than 10 million people in the U.S. suffer from movement disorders such as essential tremor, dystonia, and Parkinson's disease. Additionally, over three million people in the U.S. and over 50 million people worldwide suffer from epilepsy. One of the most effective emerging treatments for these diseases is deep brain stimulation (DBS) of the subthalamic nucleus with periodic, high frequency electric pulse trains. DBS has been federally approved for treatment of various movement disorders, with the FDA approving DBS treatment of essential tremor in 1997, Parkinson's disease in 2002, and dystonia in 2003.
Treatment of a patient using DBS requires adapting high frequency electrical pulse trains to match individualized patient needs. Such customized treatment is achieved through adjustment of stimulation parameters such as amplitude, pulse width, and repetition rate (frequency). Though research on DBS has been ongoing for over a decade, the underlying principles of DBS are still not clear. Consequently, the parameters for DBS treatment have traditionally been set based on an analysis of visual signs of symptoms.
Using visual symptoms to analyze and set electrical stimulation parameters requires that the patient, after receiving a surgical DBS implant, return to a neurologist or trained technician such that the neurologist or technician may monitor symptoms and alter parameters accordingly using an external component to wirelessly transmit instructions to the implanted neurostimulator. This process takes 3-5 hours, and must be undertaken by the patient frequently in the first 3-6 month period, and periodically after that as symptoms change. This type of system currently in place is called open-loop DBS.
The DBS process may be substantially simplified by using a dedicated microprocessor to determine the proper stimulation parameters automatically, thus obviating the need for a neurologist or technician to repeatedly conduct this task manually based on assessment of visual symptoms. This simplified process is known as closed-loop DBS.
Though the precise principles of DBS are not yet known, it is commonly understood that calibration of stimulation parameters may be reasonably obtained through measurement of one or both of two neural signals: spike signals, also known as action potentials, and local field potential (LFP) signals. One existing approach to DBS utilizes the first of these signals, spike signals, in conjunction with principal component analysis (PCA). Although this method can disentangle spike signals from each other and from background noise, it is extremely computation-intensive, making long term power supply of this type of DBS device by radio frequency or battery infeasible. Further, recent studies have shown that the latter signal, LFP, is in fact a more effective DBS feedback indicator. Current closed-loop DBS approaches are therefore too power intensive to be feasible as a long-term RF or battery-powered solution, and they fail to utilize the more effective LFP signals, instead focusing primarily on spike signals.